Now, we relax \(n\)  as a continuous variable, which will be rounded back to an integer value. From (REF ), (REF ), and (REF ), we can observe that, as \(n\)  increases, \(E^{UL, k} (n)\)  and \(E^{C, k} (n)\)  becomes larger while \(T\)  decreases. Hence, we can know that a local optimal \(n^*\)  may exist for minimizing \(f_E (n)\) . Since \(f_E (n)\)  is differentiable with respect to \(n\)  in the given range, we can find \(n^*\)  by solving \(\partial {f_E (n)}/ {\partial n} =0\)  from Fermat's Theorem [1]}. Although it is difficult to derive \(n^*\)  analytically, we can obtain it numerically using a line search method. Hence, we can find a local optimal solution, which minimizes the total energy consumption under the given target accuracy.
